cascade base generator (genesis "base" king)
A self-contained cascade data generator that adapts a curated subset of
TempoPFN's procedural synthetic time-series
priors into a single deterministic Generator(DataGenerator). Intended as the
launch/genesis king for the cascade subnet.
What's inside
| file | purpose |
|---|---|
generator.py |
class Generator(DataGenerator) — the entrypoint |
config.json |
length band, per-family mixing weights, sanitisation knobs |
requirements.txt |
hash-locked, allowlisted deps (numpy, scipy, pandas, torch, scikit-learn, gpytorch, networkx) |
tempo_gen/ |
vendored, import-rewritten TempoPFN subset (Apache-2.0) |
NOTICE, LICENSE |
TempoPFN attribution + Apache-2.0 text |
tests/ |
contract tests + a diversity sanity check |
Generator families
Ten families are mixed by weight: ForecastPFN, SineWave, SawTooth, Step,
Anomaly, Spikes, OrnsteinUhlenbeck, GP-prior, KernelSynth, CauKer. Each is
drawn at generate_length (2048) and deterministically random-cropped into the
[min_length, max_length] band for length diversity.
The GP/kernel family — GP-prior (gpytorch), KernelSynth (scikit-learn)
and CauKer (networkx + scikit-learn) — was added in v2: those deps are now
on cascade's allowlist (chain.toml [dependencies]). The TempoPFN ablation
shows this family carries a large share of the downstream signal. CauKer is
multivariate (an SCM DAG of GP-prior nodes); each channel is flattened into its
own univariate series so the emitted corpus stays 1-D. Its upstream GPU
(cupy) draw was replaced with NumPy's seeded multivariate_normal to keep the
generate path CPU-only and reproducible.
The pyo-backed audio generators remain excluded: pyo runs a real-time
audio server and seeds via hash(), both of which break the cross-process
determinism contract below (and pyo is not on the allowlist).
Determinism
The corpus is a pure function of (seed, n_series). We seed NumPy / torch /
Python random from seed, run torch on CPU with deterministic algorithms,
derive every per-family and per-series sub-seed from the master seed, and use a
separate seeded RNG for cropping. The upstream hash()-based per-generator seed
offset — salted per process by PYTHONHASHSEED — was replaced with a stable
zlib.crc32 in tempo_gen/.../abstract_classes.py, so two audit runs in
separate processes produce byte-identical corpora.
Verify / test
# from the cascade repo root, with deps installed and cascade importable
cascade verify ./base_generator # full trainer-side checks
PYTHONPATH=$PWD pytest base_generator/tests -q # contract tests
PYTHONPATH=$PWD python base_generator/tests/diversity_check.py
Config knobs (config.json)
min_length/max_length— per-series length band (defaults 64 / 2048, matchingchain.toml).generate_length— length generators are drawn at before cropping (>=max_length).batch_size— internal generation batch size (memory/throughput knob).weights— per-family mixing weights (normalised; families with weight 0 or absent are skipped).standardize— z-normalise each series (defaultfalse; off preserves scale diversity).clip_sigma— if > 0, clip each series tomean ± clip_sigma·std(default0, disabled).max_abs_value— hard magnitude clip to keep output trainer-safe finite.